Temporarily disable test_transformer_engine:test_transformer_layer[ba… #3119
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…ckward].
One of the ranks failed with the following error. This test was added as
a performance baseline, and doesn't verify any nvFuser functionality. So
I am disabling it to clean CI and will investigate it further.
```
__________________________________________________________________________________________________________________________________________________________________________________________________________________________________ test_transformer_layer[backward] ___________________________________________________________________________________________________________________________________________________________________________________________________________________________________
mpi_test = <mpi_fixtures.MpiTest object at 0x7f594feccdf0>, benchmark = <pytest_benchmark.fixture.BenchmarkFixture object at 0x7f594feccbb0>, compute_type = <ComputeType.BACKWARD: 2>
@pytest.mark.mpi
@pytest.mark.parametrize(
"compute_type",
[ComputeType.FORWARD, ComputeType.BACKWARD],
ids=["forward", "backward"],
)
def test_transformer_layer(mpi_test, benchmark, compute_type):
# Hyperparameters for GPT-3
hidden_size = 12288
num_heads = 96
ffn_hidden_size = hidden_size * 4
batch_size = 1
sequence_length = 2048
dtype = torch.bfloat16
size = mpi_test.size
rank = mpi_test.rank
torch.cuda.set_device(rank)
os.environ["MASTER_ADDR"] = "localhost"
# The default port as used by https://github.com/pytorch/pytorch/blob/45a8b5682eb69d865cbf68c7f2f689b56b4efd53/torch/csrc/distributed/c10d/TCPStore.hpp#L51.
os.environ["MASTER_PORT"] = "29500"
dist.init_process_group(
backend="nccl",
init_method="env://",
world_size=size,
rank=rank,
)
tp_group = dist.new_group()
transformer_layer = te.TransformerLayer(
hidden_size,
ffn_hidden_size,
num_heads,
set_parallel_mode=True,
tp_group=tp_group,
)
transformer_layer.to(dtype).to("cuda")
x = torch.randn(
batch_size, sequence_length, hidden_size, dtype=dtype, device="cuda"
)
match compute_type:
case ComputeType.FORWARD:
def benchmark_fn(profile):
if profile:
torch.cuda.cudart().cudaProfilerStart()
y = transformer_layer(x)
torch.cuda.synchronize()
if profile:
torch.cuda.cudart().cudaProfilerStop()
return y
# Warmup.
y = benchmark_fn(False)
assert y.size() == torch.Size([batch_size, sequence_length, hidden_size])
benchmark.pedantic(benchmark_fn, args=(True,), rounds=5)
case ComputeType.BACKWARD:
# Due to
# Lightning-AI/lightning-thunder#701, a
# limitation in TransformerEngine, we can't repeatedly call
# torch.autograd.backward to benchmark just backprop. As a
# workaround, the code below runs forward before each backprop but
# only measure the backprop time.
def setup_fn(profile):
y = transformer_layer(x)
dy = torch.rand_like(y)
torch.cuda.synchronize()
# Unlike for forward, I can't pass `profile` directly to
# `benchmark_fn` because `benchmark.pedantic` is not allowed to
# take both `setup` and `args`. Therefore, we pass `profile` to
# `setup_fn`, which in turn passes iit through to
# `benchmark_fn`.
return (y, dy, profile), {}
def benchmark_fn(y, dy, profile):
if profile:
torch.cuda.cudart().cudaProfilerStart()
torch.autograd.backward(y, dy)
torch.cuda.synchronize()
if profile:
torch.cuda.cudart().cudaProfilerStop()
# Warmup.
> args, kwargs = setup_fn(False)
tests/python/test_transformer_engine.py:123:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
tests/python/test_transformer_engine.py:102: in setup_fn
y = transformer_layer(x)
/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py:1736: in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py:1747: in _call_impl
return forward_call(*args, **kwargs)
/usr/local/lib/python3.10/dist-packages/transformer_engine/pytorch/transformer.py:677: in forward
self_attention_outputs = self.self_attention(
/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py:1736: in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py:1747: in _call_impl
return forward_call(*args, **kwargs)
/usr/local/lib/python3.10/dist-packages/transformer_engine/pytorch/attention.py:8535: in forward
projection_output = self.proj(
/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py:1736: in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py:1747: in _call_impl
return forward_call(*args, **kwargs)
/usr/local/lib/python3.10/dist-packages/torch/_dynamo/eval_frame.py:629: in _fn
return fn(*args, **kwargs)
/usr/local/lib/python3.10/dist-packages/transformer_engine/pytorch/module/linear.py:1017: in forward
out = linear_fn(*args)
/usr/local/lib/python3.10/dist-packages/torch/autograd/function.py:575: in apply
return super().apply(*args, **kwargs) # type: ignore[misc]
/usr/local/lib/python3.10/dist-packages/transformer_engine/pytorch/module/linear.py:360: in forward
out, _ = allreduce(out, tp_group)
/usr/local/lib/python3.10/dist-packages/transformer_engine/pytorch/distributed.py:886: in allreduce
handle = torch.distributed.all_reduce(input_, group=tp_group, async_op=async_op)
/usr/local/lib/python3.10/dist-packages/torch/distributed/c10d_logger.py:83: in wrapper
return func(*args, **kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
tensor = tensor([[ 2.3438, -1.4609, -1.5234, ..., 2.1250, -3.9375, -2.3906],
[ 2.4844, 0.3125, 2.2812, ..., -1.906...],
[-1.4297, -8.3125, -1.8516, ..., -7.2812, 1.1094, 2.5625]],
device='cuda:3', dtype=torch.bfloat16), op = <RedOpType.SUM: 0>, group = <torch.distributed.distributed_c10d.ProcessGroup object at 0x7f5afd67e430>, async_op = False
@_exception_logger
def all_reduce(tensor, op=ReduceOp.SUM, group=None, async_op=False):
"""
Reduces the tensor data across all machines in a way that all get the final result.
After the call ``tensor`` is going to be bitwise identical in all processes.
Complex tensors are supported.
Args:
tensor (Tensor): Input and output of the collective. The function
operates in-place.
op (optional): One of the values from
``torch.distributed.ReduceOp``
enum. Specifies an operation used for element-wise reductions.
group (ProcessGroup, optional): The process group to work on. If None,
the default process group will be used.
async_op (bool, optional): Whether this op should be an async op
Returns:
Async work handle, if async_op is set to True.
None, if not async_op or if not part of the group
Examples:
>>> # xdoctest: +SKIP("no rank")
>>> # All tensors below are of torch.int64 type.
>>> # We have 2 process groups, 2 ranks.
>>> device = torch.device(f'cuda:{rank}')
>>> tensor = torch.arange(2, dtype=torch.int64, device=device) + 1 + 2 * rank
>>> tensor
tensor([1, 2], device='cuda:0') # Rank 0
tensor([3, 4], device='cuda:1') # Rank 1
>>> dist.all_reduce(tensor, op=ReduceOp.SUM)
>>> tensor
tensor([4, 6], device='cuda:0') # Rank 0
tensor([4, 6], device='cuda:1') # Rank 1
>>> # All tensors below are of torch.cfloat type.
>>> # We have 2 process groups, 2 ranks.
>>> tensor = torch.tensor([1+1j, 2+2j], dtype=torch.cfloat, device=device) + 2 * rank * (1+1j)
>>> tensor
tensor([1.+1.j, 2.+2.j], device='cuda:0') # Rank 0
tensor([3.+3.j, 4.+4.j], device='cuda:1') # Rank 1
>>> dist.all_reduce(tensor, op=ReduceOp.SUM)
>>> tensor
tensor([4.+4.j, 6.+6.j], device='cuda:0') # Rank 0
tensor([4.+4.j, 6.+6.j], device='cuda:1') # Rank 1
"""
_check_single_tensor(tensor, "tensor")
if _rank_not_in_group(group):
_warn_not_in_group("all_reduce")
return
if tensor.is_complex():
if not supports_complex(op):
raise ValueError(f"all_reduce does not support {op} on complex tensors")
tensor = torch.view_as_real(tensor)
opts = AllreduceOptions()
opts.reduceOp = op
if group is None:
group = _get_default_group()
if group in _world.pg_coalesce_state.keys():
# We are in coalescing context, do not issue single operation, just append a collective representation
coll = _CollOp(all_reduce, tensor, None, op, None)
_world.pg_coalesce_state[group].append(coll)
if async_op:
return _IllegalWork()
else:
return None
> work = group.allreduce([tensor], opts)
E torch.distributed.DistBackendError: NCCL error in: /opt/pytorch/pytorch/torch/csrc/distributed/c10d/NCCLUtils.hpp:317, unhandled system error (run with NCCL_DEBUG=INFO for details), NCCL version 2.22.3
E ncclSystemError: System call (e.g. socket, malloc) or external library call failed or device error.
E Last error:
E socketStartConnect: Connect to 10.120.104.58<41447> failed : Software caused connection abort
/usr/local/lib/python3.10/dist-packages/torch/distributed/distributed_c10d.py:2603: DistBackendError
```
|
!build |
rdspring1
approved these changes
Oct 7, 2024
This was referenced Oct 7, 2024
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…ckward].
One of the ranks failed with the following error. This test was added as a performance baseline, and doesn't verify any nvFuser functionality. So I am disabling it to clean CI and will investigate it further.